{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multimedia Security Experiment 3\n"
     ]
    }
   ],
   "source": [
    "print(\"Multimedia Security Experiment 3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import myipf\n",
    "#from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 用来正常显示中文\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False # 用来正常显示负号\n",
    "%config InlineBackend.figure_format = \"svg\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.图像再量化并对比显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Created with matplotlib (https://matplotlib.org/) -->\n",
       "<svg height=\"128.808088pt\" version=\"1.1\" viewBox=\"0 0 359.964706 128.808088\" width=\"359.964706pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 128.808088 \n",
       "L 359.964706 128.808088 \n",
       "L 359.964706 0 \n",
       "L 0 0 \n",
       "z\n",
       "\" style=\"fill:none;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g clip-path=\"url(#p5dc6400144)\">\n",
       "    <image height=\"99\" id=\"image7ff7f791dc\" transform=\"scale(1 -1)translate(0 -99)\" width=\"99\" x=\"7.2\" xlink:href=\"data:image/png;base64,\n",
       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      ],
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = plt.imread(\"images/lena.bmp\")\n",
    "grayImg = np.dot(img, [0.299,0.587,0.114]) # 加权平均后转为单通道灰度图像\n",
    "_32 = myipf.requalification(grayImg, 8)\n",
    "_02 = myipf.requalification(grayImg, 128)\n",
    "plt.subplot(1, 3, 1), plt.imshow(grayImg, cmap='gray'), plt.title(\"原图像\"), plt.axis(\"off\")\n",
    "plt.subplot(1, 3, 2), plt.imshow(_32, cmap='gray'), plt.title(\"步长8，即量化等级32\"), plt.axis(\"off\")\n",
    "plt.subplot(1, 3, 3), plt.imshow(_02, cmap='gray'), plt.title(\"步长128，即量化等级2\"), plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.再量化图像与原图像的均方差以及峰值信噪比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方差： 5.377342997072\n",
      "峰值信噪比： 40.82512621771343\n",
      "均方差： 1346.0837606610717\n",
      "峰值信噪比： 16.84008275971706\n"
     ]
    }
   ],
   "source": [
    "myipf.calcMSEPSNR(grayImg, _32)\n",
    "myipf.calcMSEPSNR(grayImg, _02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到量化等级为32得到的图像与原图的峰值信噪比为41dB，失真小；\n",
    "量化等级为2得到的图像与原图的峰值信噪比为17dB，失真大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.图像的改变\n",
    "\n",
    "量化等级为2，就相当于二值化了，不过我写的再量化函数并没有取极值，而是取中间值，即量化等级为2时图像两个像素值分别为64和192，可以非常明显的看到伪轮廓，毕竟是64到192的突变。\n",
    "按我的函数来看，量化等级4一下可以观察到非常明显的伪轮廓，等级4到等级16图像中也存在大大小小的伪轮廓。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "    <!-- 测试 -->\n",
       "    <defs>\n",
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      ],
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_08 = myipf.requalification(grayImg, 16)\n",
    "_04 = myipf.requalification(grayImg, 64)\n",
    "plt.subplot(1, 3, 1), plt.imshow(grayImg, cmap='gray'), plt.title(\"原图像\"), plt.axis(\"off\")\n",
    "plt.subplot(1, 3, 2), plt.imshow(_08, cmap='gray'), plt.title(\"测试\"), plt.axis(\"off\")\n",
    "plt.subplot(1, 3, 3), plt.imshow(_02, cmap='gray'), plt.title(\"步长64，即量化等级4\"), plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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